36 citations · 73 across the 12 of their papers we have counts for
12 papers
AEF-Econ: Toward Plug-and-Play Socioeconomic Foundation Embeddings from AlphaEarth for Urban Remote Sensing
Shuyang Hou, Ziqi Liu, Haoyue Jiao +7
AlphaEarth Foundations (AEF) unify global remote sensing foundation embeddings through multimodal self-supervised learning, but their pretraining focuses on physical land-surface s…
Slum Detection and Density Mapping with AlphaEarth Foundations: A Representation Learning Evaluation Across 12 Global Cities
Shuyang Hou, Ziqi Liu, Haoyue Jiao +7
Pixel-level slum mapping has long been constrained by limited cross-city generalisation, the absence of continuous density estimation, and weak global comparability. AlphaEarth Fou…
SubTokenTest: A Practical Benchmark for Real-World Sub-token Understanding
Shuyang Hou, Yi Hu, Muhan Zhang
Recent advancements in large language models (LLMs) have significantly enhanced their reasoning capabilities. However, they continue to struggle with basic character-level tasks, s…
GeoSQL-Eval: First Evaluation of LLMs on PostGIS-Based NL2GeoSQL Queries
Shuyang Hou, Haoyue Jiao, Ziqi Liu +5
Large language models (LLMs) have shown strong performance in natural language to SQL (NL2SQL) tasks within general databases. However, extending to GeoSQL introduces additional co…
GeoJSEval: An Automated Evaluation Framework for Large Language Models on JavaScript-Based Geospatial Computation and Visualization Code Generation
Guanyu Chen, Haoyue Jiao, Shuyang Hou +6
With the widespread adoption of large language models (LLMs) in code generation tasks, geospatial code generation has emerged as a critical frontier in the integration of artificia…
AutoGEEval++: A Multi-Level and Multi-Geospatial-Modality Automated Evaluation Framework for Large Language Models in Geospatial Code Generation on Google Earth Engine
Shuyang Hou, Zhangxiao Shen, Huayi Wu +10
Geospatial code generation is becoming a key frontier in integrating artificial intelligence with geo-scientific analysis, yet standardised automated evaluation tools for this task…
AutoGEEval: A Multimodal and Automated Framework for Geospatial Code Generation on GEE with Large Language Models
Shuyang Hou, Zhangxiao Shen, Huayi Wu +8
Geospatial code generation is emerging as a key direction in the integration of artificial intelligence and geoscientific analysis. However, there remains a lack of standardized to…
GEE-OPs: An Operator Knowledge Base for Geospatial Code Generation on the Google Earth Engine Platform Powered by Large Language Models
Shuyang Hou, Jianyuan Liang, Anqi Zhao +1
As the scale and complexity of spatiotemporal data continue to grow rapidly, the use of geospatial modeling on the Google Earth Engine (GEE) platform presents dual challenges: impr…
Chain-of-Programming (CoP) : Empowering Large Language Models for Geospatial Code Generation
Shuyang Hou, Haoyue Jiao, Zhangxiao Shen +5
With the rapid growth of interdisciplinary demands for geospatial modeling and the rise of large language models (LLMs), geospatial code generation technology has seen significant…
Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation Tasks Using Large Language Models
Shuyang Hou, Anqi Zhao, Jianyuan Liang +2
The rise of spatiotemporal data and the need for efficient geospatial modeling have spurred interest in automating these tasks with large language models (LLMs). However, general L…
GeoCode-GPT: A Large Language Model for Geospatial Code Generation Tasks
Shuyang Hou, Zhangxiao Shen, Anqi Zhao +5
The increasing demand for spatiotemporal data and modeling tasks in geosciences has made geospatial code generation technology a critical factor in enhancing productivity. Although…
Can Large Language Models Generate Geospatial Code?
Shuyang Hou, Zhangxiao Shen, Jianyuan Liang +4
With the growing demand for spatiotemporal data processing and geospatial modeling, automating geospatial code generation has become essential for productivity. Large language mode…